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Page Rank


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Page Rank

  1. 1. PageRank <ul><li>What is PageRank </li></ul><ul><li>Why PageRank </li></ul><ul><li>Related work and problems </li></ul><ul><li>Link Structure of the Web </li></ul><ul><li>Definition of PageRank </li></ul><ul><li>Dangling Links </li></ul><ul><li>Implementation </li></ul>
  2. 2. PageRank(cont.) <ul><li>What is PageRank </li></ul><ul><li>In order to measure the relative importance of web pages, PageRank is proposed. It is a method for computing a ranking for every web page based on the graph of the web. </li></ul>
  3. 3. PageRank(cont.) <ul><li>Why PageRank </li></ul><ul><li>__The World Wide Web is very large and </li></ul><ul><li>heterogeneous. </li></ul><ul><li>__Search engines on the Web must also contend </li></ul><ul><li>with inexperienced users and pages engineered </li></ul><ul><li>to manipulate search engine ranking functions. </li></ul><ul><li>Unlike “flat” document collections, the World </li></ul><ul><li>Wide Web is hypertext and provides considerable </li></ul>
  4. 4. PageRank(cont.) <ul><li>auxiliary information on top of the text of the web pages, such as link structure and link text. We can take advantage of the link structure of the web to produce a PageRank of every web page. It helps search engines and users quickly make sense of the vast heterogeneity of the World Wide Web. </li></ul>
  5. 5. PageRank (Cont.) <ul><li>Related work and problems </li></ul><ul><li>__B acklink counts </li></ul><ul><li>Problem: for example, if a web page has a link off the Yahoo home page, it may be just one link but it is very important one. This page should be ranked higher than many pages with more </li></ul><ul><li>backlinks but from obscure places. </li></ul><ul><li>__T he ranks and numbers of backlinks </li></ul><ul><li>This covers both the case that when a page has many backlinks and when a page has a few highly ranked backlinks. Let u be a webpage, </li></ul>
  6. 6. PageRank (Cont.)
  7. 7. PageRank (Cont.) <ul><li>be the set of pages that point to u. be the number of </li></ul><ul><li>links from u and let c be a factor used for normalization, then </li></ul><ul><li>a simplified version of PageRank: </li></ul>
  8. 8. PageRank (Cont.) <ul><li>Problem: may form a rank sink. Consider two web pages </li></ul><ul><li>that point to each other but to no other page. And if there is </li></ul><ul><li>some web page which points to one of them. Then, during </li></ul><ul><li>iteration, this loop will accumulate rank but never distribute </li></ul><ul><li>any rank. The loop forms a sort of trap called a rank sink. </li></ul>
  9. 9. PageRank (Cont.) <ul><li>Link Structure of the Web </li></ul><ul><li>___ Pages are as nodes </li></ul><ul><li>___Links are as edges (outedges and inedges) </li></ul><ul><li>Every page has some forward links (outedges) and </li></ul><ul><li>backlinks (inedges). We can never know whether we </li></ul><ul><li>have found all the backlinks of a particular page but if we </li></ul><ul><li>have downloaded it, we know all of its forward links at that </li></ul><ul><li>time. PageRank handles both cases and everything in </li></ul><ul><li>between by recursively propagating weights through the </li></ul><ul><li>link structure of the web. </li></ul>
  10. 10. PageRank(Cont.) <ul><li>Definition of PageRank </li></ul><ul><li>We assume page A has pages T1 ,…, Tn , which </li></ul><ul><li>point to it. The parameter d is a damping factor </li></ul><ul><li>which can be set between 0 and 1(usually d is </li></ul><ul><li>set to 0.85). Also C(A) is defined as the number </li></ul><ul><li>of links going out of page A. The PageRank of </li></ul><ul><li>page A is given as follows: </li></ul>
  11. 11. PR(A)=(1-d) + d*(PR(T1)/C(T1) + PR(T2)/C(T2) + PR(T3)/C(T3)) =0.15+0.85*(0.5/3 + 0.3/4+ 0.1/5) 3 4 5 2 T1 PR=0.5 T2 PR=0.3 T3 PR=0.1 A
  12. 12. PageRank(Cont.) <ul><li>Let A be a square matrix with the rows and column </li></ul><ul><li>corresponding to web pages. Let if </li></ul><ul><li>there is an edge from u to v and if not. If </li></ul><ul><li>we treat R as a vector over web pages, then we </li></ul><ul><li>have . Here E is a uniform vector. </li></ul><ul><li>Since , we can rewrite this as </li></ul><ul><li>. So R is an eigenvector of </li></ul>with eigenvalue d .
  13. 13. PageRank(Cont.) <ul><li>Dangling Links </li></ul><ul><li>Dangling links are simply links that point to any page with </li></ul><ul><li>no outgoing links. They affect the model because it is not </li></ul><ul><li>clear where their weights should be distributed, and there </li></ul><ul><li>are a large number of them. Because they do not affect </li></ul><ul><li>the ranking of any other page directly, we simply remove </li></ul><ul><li>them from the system until all the PageRanks are </li></ul><ul><li>calculated. After all the PageRanks are calculated, they </li></ul><ul><li>can be added back in, without affecting things significantly. </li></ul>
  14. 14. PageRank(Cont.) <ul><li>Implementation </li></ul><ul><li>Sort the link structure by ParentID </li></ul><ul><li>Remove dangling links from the link database </li></ul><ul><li>Make an initial assignment of the ranks </li></ul><ul><li>Memory is allocated for the weights for every page </li></ul><ul><li>After the weights have converged, add the dangling links back in and recompute the rankings </li></ul>